Streamlined Poststroke Treatment Order Sets During the SARS-CoV-2 Pandemic
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Standard poststroke treatment monitoring protocols are made problematic during the coronavirus disease 2019 (COVID-19) pandemic by the frequency of patient assessments, requiring repeated donning and doffing procedures in a short interval of time. METHODS: A streamlined poststroke treatment protocol was developed to limit frequency of patient encounters while maximizing the yield of each encounter by grouping together different components of poststroke care into single bedside visits. RESULTS: Streamlined order sets were developed late March 2020. During the first 6 weeks following implementation, 70 patients were admitted to a geographically defined designated warm COVID-19 unit with modified poststroke care order sets. Of these, 33 (47.1%) patients received acute reperfusion therapy. All but 3 patients evolved favorably with either stable or improving National Institutes of Health Stroke Scale at 24 hours. In the 3 patients who experienced early neurological deterioration, none were found to be attributable to insufficient patient monitoring. CONCLUSIONS: Adapting preexisting poststroke care protocols may be necessary while the risk of COVID-19 infection remains high. We propose a streamlined approach to facilitate poststroke monitoring in patients with stroke with unknown COVID status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".